Plastic pollution is a critical environmental issue, and detecting and monitoring plastic litter is crucial to mitigate its impact. This paper presents the methodology of mapping street-level litter, focusing primarily on plastic waste and the location of trash bins. Our methodology involves employing a deep learning technique to identify litter and trash bins from street-level imagery taken by a camera mounted on a vehicle. Subsequently, we utilized heat maps to visually represent the distribution of litter and trash bins throughout cities. Additionally, we provide details about the creation of an open-source dataset ("pLitterStreet") which was developed and utilized in our approach. The dataset contains more than 13,000 fully annotated images collected from vehicle-mounted cameras and includes bounding box labels. To evaluate the effectiveness of our dataset, we tested four well known state-of-the-art object detection algorithms (Faster R-CNN, RetinaNet, YOLOv3, and YOLOv5), achieving an average precision (AP) above 40%. While the results show average metrics, our experiments demonstrated the reliability of using vehicle-mounted cameras for plastic litter mapping. The "pLitterStreet" can also be a valuable resource for researchers and practitioners to develop and further improve existing machine learning models for detecting and mapping plastic litter in an urban environment. The dataset is open-source and more details about the dataset and trained models can be found at https://github.com/gicait/pLitter.

翻译：塑料污染是一个关键的环境问题，检测和监测塑料垃圾对于减轻其影响至关重要。本文介绍了街道级垃圾制图的方法，重点聚焦塑料废弃物和垃圾桶的位置。我们的方法采用深度学习技术，从安装在车辆上的摄像头拍摄的街道级图像中识别垃圾和垃圾桶。随后，我们利用热图直观展示垃圾和垃圾桶在城市中的分布情况。此外，我们详细介绍了在我们的方法中开发和使用的开源数据集（"pLitterStreet"）的创建过程。该数据集包含从车载摄像头收集的超过13,000张完全标注的图像，并包含边界框标签。为了评估数据集的有效性，我们测试了四种知名的先进目标检测算法（Faster R-CNN、RetinaNet、YOLOv3和YOLOv5），实现了超过40%的平均精度（AP）。尽管结果指标处于平均水平，但我们的实验证明了使用车载摄像头进行塑料垃圾制图的可靠性。"pLitterStreet"也可成为研究人员和从业者开发和改进现有机器学习模型以在城市环境中检测和制图塑料垃圾的宝贵资源。该数据集是开源的，有关数据集和训练模型的更多详细信息可在https://github.com/gicait/pLitter获取。